ANALISIS ATTRACTORS NON-LINIER PADA SINYAL HEART RATE VARIABILITY (HRV) UNTUK DETEKSI AWAL PENYAKIT JANTUNG KORONER
Keywords:
Biomedical EngineeringAbstract
Penyakit jantung koroner (Coronary Artery Disease/CAD) merupakan salah satu penyebab kematian tertinggi di dunia yang seringkali terdeteksi pada tahap lanjut. Oleh karena itu, diperlukan metode deteksi dini yang cepat, non-invasif, dan efisien. Salah satu pendekatan yang potensial adalah pemanfaatan sinyal Heart Rate Variability (HRV). Namun, metode analisis HRV konvensional berbasis pendekatan linier sering kali tidak mampu menangkap dinamika kompleks jantung yang bersifat non-linier. Penelitian ini mengusulkan pendekatan berbasis rekonstruksi ruang fase untuk mengungkap pola dinamis non-linier dari parameter HRV, yaitu SDRR (Standar Deviation of RR intervals) dan RMSSD (Root Mean Square of Successive Differences), pada dua lead sinyal elektrokardiogram (EKG) (Lead II dan V1). Gambar hasil Phase Space Reconstruction (PSR) dianalisis secara morfologis melalui uji Kolmogorov–Smirnov dua sampel, serta diklasifikasikan menggunakan dua arsitektur dari Convolutional Neural Network (CNN), yakni VGG-16 dan LeNet. Hasil penelitian menunjukkan bahwa model CNN berbasis VGG-16 mampu mencapai akurasi yang tinggi dalam membedakan pola PSR antara pasien CAD dan individu normal. Pengujian Kolmogorov-Smirnov juga mendukung perbedaan signifikan distribusi ukuran geometris attractors antara kelas CAD dan normal.
Kata kunci: Coronary Artery Disease, Heart Rate Variability, LeNet, Phase Space Reconstruction, VGG-16
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